Uncertainty analysis in building performance simulation for design support

Hopfe, Christina J.; Hensen, Jlm Jan

doi:10.1016/j.enbuild.2011.06.034

Cited by 423 publications

(288 citation statements)

References 14 publications

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“…Uncertainties in scenario conditions are very different from physical and design uncertainties in the sense that they can change during the building's life time [19]. Taking scenario uncertainties into account is relevant to design decision support, in particular when considering the design robustness and (future) adaptability of a building.…”

Section: (I) Limiting the Risk By Controlling The Scenario Uncertaintiesmentioning

confidence: 99%

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Multi-criteria decision making under uncertainty in building performance assessment

Hopfe

Augenbroe

Hensen

2013

Building and Environment

Self Cite

134

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Multi-criteria decision making under uncertainty in building performance assessment. Building and Environment, 69, Additional Information:• This is the author's version of a work that was accepted for publication in Building and Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published at: AbstractBuilding performance assessment is complex, as it has to respond to multiple criteria.Objectives originating from the demands that are put on energy consumption, acoustical performance, thermal occupant comfort, indoor air quality and many other issues must all be reconciled. An assessment requires the use of predictive models that involve numerous design and physical parameters as their inputs. Since these input parameters, as well as the models that operate on them, are not precisely known, it is imprudent to assume deterministic values for them. A more realistic approach is to introduce ranges of uncertainty in the parameters themselves, or in their derivation, from underlying approximations. In so doing, it is recognized that the outcome of a performance assessment is influenced by many sources of uncertainty.As a consequence of this approach the design process is informed by assessment outcomes that produce probability distributions of a target measure instead of its deterministic value. In practice this may lead to a "well informed" analysis but not necessarily to a straightforward, cost effective and efficient design process. This paper discusses how design decision making can be based on uncertainty assessments. A case study is described focusing on a discrete decision that involves a choice between two HVAC system designs. Analytical hierarchy process (AHP) including uncertainty information is used to arrive at a rational decision. In this approach, key performance indicators such as energy efficiency, thermal comfort and others are ranked according to their importance and preferences. This process enables a clear group consensus based choice of one of the two options. The research presents a viable means of collaboratively ranking complex design options based on stakeholder's preferences and considering the uncertainty involved in the designs. In so doing it provides important feedback to the design team.

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Section: (I) Limiting the Risk By Controlling The Scenario Uncertaintiesmentioning

confidence: 99%

“…The material properties are identical for both options and therefore not explained in more detail. For more information please refer to [19].…”

Section: Design Option 2: Heating/ Cooling Storagementioning

confidence: 99%

Multi-criteria decision making under uncertainty in building performance assessment

Hopfe

Augenbroe

Hensen

2013

Building and Environment

Self Cite

134

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“…Furthermore, in many analyses or designs the explicit evaluation of the result for different scenarios -be they defined based on user behaviour, economic parameters, climate change, ... -may be wanted as suggested by Hopfe and Hensen [15]. This asks for the formulation and application of Monte Carlo multi-layered uncertainty schemes.…”

Section: Paper Objectivesmentioning

confidence: 99%

Probabilistic design and analysis of building performances: Methodology and application example

Gelder

Janssen

Roels

2014

Energy and Buildings

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Both building performance analysis and multi-criteria performance optimisation often use deterministic simulations. Since many influencing parameters are generally inherently uncertain, this may lead to unreliable predictions of design impact. Therefore, this paper proposes a probabilistic analysis and design method to incorporate these uncertainties. The embedded Monte Carlo based uncertainty and sensitivity analyses investigate the output distributions. To greatly reduce computational efforts, meta-models can be incorporated, replacing the original model. Additionally, multi-layered sampling schemes are used to subject all design options to the same uncertainties and to check the validity of optimisation results for potential scenarios. Since reliability is a key aspect in this methodology, the paper also focuses on output convergence and method reliability.To optimise both average performances and spread, effectiveness ε and robustness R P are introduced as output uncertainty indicators, inspired by robust design. Here, effectiveness is defined as the ability of the design option to optimise the performance, while robustness is defined as the ability to stabilise this performance for the entire range of input uncertainties.The successive methodology steps are explained using a simplified application example.

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“…come from domain experts in building physics. More recently, Hopfe and Hensen [87] analyzed energy performance (energy consumption and thermal comfort) and considered three different groups of uncertainties for building design: (i) physical uncertainties inherent in physical properties, which appear in quantified measurements; (ii) design uncertainties, such as changes in the room geometry or the window size; (iii) scenario uncertainties that are linked to building usage. The results of their study confirmed the added value and usefulness of integrating uncertainty analysis in building performance simulations [87].…”

Section: Uncertainty and Imprecisionmentioning

confidence: 99%

Data science for building energy management: A review

Molina-Solana

Ros

Ruiz

et al. 2017

Renewable and Sustainable Energy Reviews

227

108

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The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.

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Uncertainty analysis in building performance simulation for design support

Cited by 423 publications

References 14 publications

Multi-criteria decision making under uncertainty in building performance assessment

Multi-criteria decision making under uncertainty in building performance assessment

Probabilistic design and analysis of building performances: Methodology and application example

Data science for building energy management: A review

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